Discover 3D stratigraphic models that best explain measured logs by leveraging VQ-VAE and data clustering

There is a substantial need for exploration and field development workflows that generate 3D stratigraphic models, which can be used to explain well log data for making drilling and measurement acquisition decisions. This abstract presents a workflow designed for this purpose; i.e., locate the ensemble of 3D stratigraphic models that can explain observations. 3D stratigraphic models spanning a spectrum of depositional environments are stored in a datastore. This datastore models have encoded indexes of well log signatures. This encoding is obtained using the Vector Quantization Variational Autoencoder (VQ-VAE) approach. User-supplied well logs are also encoded with VQ-VAE to allow for a quick search in this datastore by looking for the nearest clustering centroids.